Bench to Biobank

Autoimmune drug discovery often fails when a gene that looks important in a cell experiment doesn't hold up in real people. We built a reproducible pipeline that takes drug-target hypotheses from the Marson-lab genome-scale CD4+ T-cell Perturb-seq screen all the way to individual-level human genomes in All of Us. Every regulator passes through five independent evidence layers: bench causality (CRISPRi knock-down that calms T-cell activation) → druggability → human GWAS → biobank PheWAS (FinnGen + GWAS Catalog, cross-ancestry check in BioBank Japan) → individual-level genomes in All of Us. From ~4,000 regulators, 2,771 pass the bench filter and 86 are druggable and disease-linked. What we found: the method works (PTPN22 and TYK2 controls replicate); IL23R validates for inflammatory bowel disease in the right disease (OR 0.73, P 4.4x10^-5); the three novel lupus receptor hypotheses return honest, well- controlled results; and the pipeline catches its own false positives with a nearest-gene stress test (e.g. our flagship "hidden hit" maps to a famous neighbor, not our gene). It also surfaced ATP2B1 as an SLE-protective lead, then honestly down-weighted it when its genetics turned out cardiometabolic. Why it matters: the discrete result is not "three lupus targets" — it's a reproducible bench-to-biobank pipeline that confirms a real target, gives honest nulls, and catches its own false positives. It's packaged as an open, config- driven tool (bench2biobank) that runs on any perturbation screen. Built entirely in Claude Science.

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